Method of constructing artificial mark for autonomous driving, apparatus and method of determining position of intelligent system using artifical mark and intelligent system employing the same

ABSTRACT

A method for constructing an artificial mark for autonomous driving of an intelligent system, an apparatus and method for determining the location of an intelligent system using the artificial mark, and an intelligent system employing the same. The apparatus and method for determining the location of an intelligent system includes a projective invariant calculator which calculates a projective invariant of an artificial mark detected from an image taken for a driving place; a search unit which stores a database of indices according to a combination of colors of polygons included in the artificial mark, projective invariants of the artificial marks, and global location information of the artificial marks in the driving place, and searches the database by the calculated projective invariant for obtaining the global location information of the detected artificial mark; and a position information analyzer which analyzes the position of the intelligent system by using the global location information of the detected artificial mark and location information between the intelligent system and the detected artificial mark.

This is a divisional of application Ser. No. 10/919,493 filed Aug. 17,2004. The entire disclosure of the prior application, application Ser.No. 10/919,493, is hereby incorporated by reference.

This application claims the priority of Korean Patent Application Nos.2003-57727, filed on Aug. 20, 2003 and 2004-62604, filed on Aug. 9,2004, in the Korean Intellectual Property Office, the disclosure ofwhich is incorporated herein in its entirety by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method for constructing an artificialmark for autonomous driving of an intelligent system, an apparatus andmethod for determining the position of an intelligent system using theartificial mark, and an intelligent system employing the same.

2. Description of the Related Art

As intelligent systems such as unmanned aerial vehicles, unmannedvehicle systems, mobile robots, intelligent transportation systems havebeen attracting increasingly more attentions recently, research anddevelopment activities for the systems are briskly performed. Inparticular, a variety of methods have been suggested for localizationmethod by which an intelligent system recognizes its location. Forlocalization, global positioning systems are used outdoors, while toolssuch as guided rails, active landmarks, passive landmarks, and naturallandmarks are usually used indoors. Among the indoor localization tools,maintenance and management of guided rails, active landmarks, andnatural landmarks are difficult and less practical and therefore thepassive landmarks are widely used.

Regarding the passive landmarks, a variety of shapes have beensuggested. As the most representative case among them, there is a methodby which a quadrangle plane with a predetermined size is divided into apredetermined number of smaller quadrangles, then the smallerquadrangles are binarized, and according to the binary codes of a mark,place information or direction information is indicated. However, ifthis passive marks are used, it takes much time for an intelligentsystem to pick up an image and detect a next mark, and because ofcolorless shapes, distinctive power is degraded. In addition, a camerainstalled in the intelligent system can provide inaccurate locationinformation depending on the location at which the camera finds a marksuch that there are restrictions in using the marks.

SUMMARY OF THE INVENTION

The present invention provides a method for generating an artificialmark which can be detected from an image taken by a camera installed inan intelligent system regardless of a location at which the camera looksat the artificial mark.

The present invention also provides an apparatus and method fordetermining the location of an intelligent system using an artificialmark which can be detected from an image taken by a camera installed inthe intelligent system regardless of a location at which the cameralooks at the artificial mark.

The present invention also provides an intelligent system employing theapparatus and method for determining a location using the artificialmark.

According to an aspect of the present invention, there is provided amethod for constructing an artificial mark, comprising: providing aplane with a first color; and arranging a plurality of polygons in theplane, which have colors different from the first color and different toeach other, wherein the artificial mark is discriminated from each otheraccording to a combination of the colors.

According to another aspect of the present invention, there is providedan apparatus for determining a position of an intelligent systemincluding: a projective invariant calculator which calculates aprojective invariant of an artificial mark detected from an image takenfor a driving place; a search unit which stores a database of indicesaccording to a combination of colors of polygons included in theartificial mark, projective invariants of the artificial marks, andglobal location information of the artificial marks in the drivingplace, and searches the database by the calculated projective invariantfor obtaining the global location information of the detected artificialmark; and a position information analyzer which analyzes the location ofthe intelligent system by using the global location information of thedetected artificial mark and location information between theintelligent system and the detected artificial mark.

According to still another aspect of the present invention, there isprovided a method of determining a position of an intelligent systemincluding: providing a database of indices according to a combination ofcolors of polygons included in the artificial mark, projectiveinvariants of the artificial marks; calculating a projective invariantof an artificial mark detected from an image taken for a driving placeand searching the database by the calculated projective invariant forobtaining the global location information of the detected artificialmark; and analyzing the position of the intelligent system by using theglobal location information of the detected artificial mark and locationinformation between the intelligent system and the detected artificialmark.

According to yet still another aspect of the present invention, there isprovided an intelligent system including: an image pickup unit whichpickups an image taken for a driving place; a main control unit whichcalculates a projective invariant of an artificial mark detected from animage taken for a driving place and analyzes the position of theintelligent system using global location information of the detectedartificial mark in the driving place obtained by the calculatedprojective invariant and location information between the intelligentsystem and the detected artificial mark; and a driving control unitwhich controls driving of the intelligent system according to thelocation information of the intelligent system analyzed in the maincontrol unit.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features and advantages of the present inventionwill become more apparent by describing in detail exemplary embodimentsthereof with reference to the attached drawings in which:

FIGS. 1A and 1B are diagrams showing the shape of an artificial markaccording to a first and a second preferred embodiments of the presentinvention, respectively;

FIGS. 2A and 2B are diagrams showing the shape of an artificial markaccording to a third and a fourth preferred embodiments of the presentinvention, respectively;

FIG. 3 is a block diagram showing the structure of an example of anintelligent system to which the present invention is applied;

FIG. 4 is a view of a pin-hall camera model of the image pickup unit ofFIG. 3;

FIG. 5 is a view showing conditions to obtain a linear model of theimage pickup unit of FIG. 3;

FIG. 6 is a flowchart of the steps performed by a position recognitionmethod of an intelligent system according to the present invention;

FIG. 7 shows a 2-dimensional artificial mark installed in a drivingplace, the coordinate system on a taken image, and the projectiverelation;

FIG. 8 shows a 1-dimensional artificial mark installed in a drivingplace, the coordinate system on a taken image, and the projectiverelation;

FIG. 9 is a diagram showing projective invariants with respect to shapesof artificial marks;

FIGS. 10A through 10D are diagrams showing the results of experiments ofartificial mark recognition according to the present invention;

FIG. 11 is a diagram explaining an embodiment of a method for analyzingthe location information of an autonomous vehicle in FIG. 6;

FIG. 12 is a diagram explaining another embodiment of a method foranalyzing the location information of an autonomous vehicle in FIG. 6;and

FIG. 13 is a diagram showing the results of detection by CONDENSATIONalgorithm when an artificial mark is rotated, slanted, translated, andscaled, and included in a plurality of objects.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, the present invention will be described in detail byexplaining preferred embodiments of the invention with reference to theattached drawings.

FIGS. 1A and 1B are diagrams showing the shape of an artificial markaccording to a first and a second preferred embodiments of the presentinvention, respectively. The artificial mark shown in FIG. 1A is formedwith first through fifth polygons 121 through 129 that are arranged2-dimensionally on a plane board 110 with a predetermined height (H) andwidth (W), and have colors different to each other. The plane board 110has a color different from the colors of the first through fifthpolygons 121 through 129. Here, the shapes of the first through fifthpolygons 121 through 129 are round and the number of polygons is 5, butthe mark is not limited to this and the number of arbitrary polygons maybe properly determined. Also, the quadrangle shape of the plane board110 is shown as an example here, but it does not matter whether or notthe shape is round. Here, by changing the arrangement order of the firstthrough fifth polygons 121 through 129, 120 (=5!) shapes can beexpressed.

The artificial mark shown in FIG. 1B is formed with first through fifthpolygons 141 through 149 that are arranged on a plane board 130 with apredetermined height (H) and width (W), and have colors different toeach other, and side quadrangles 151 and 153 disposed on both sides ofthe plane board 130. The plane board 130 has a color different from thecolors of the first through fifth polygons 141 through 149. The sidequadrangles 151 and 153 have an identical color that is different fromthe colors of the plane board 130 and the first through fifth polygons141 through 149. As the first embodiment shown in FIG. 1A, in FIG. 1B,the shapes of the first through fifth polygons 141 through 149 are roundand the number of polygons is 5, but the mark is not limited to this andthe number of arbitrary polygons may be properly determined.

FIGS. 2A and 2B are diagrams showing the shape of an artificial markaccording to a third and a fourth preferred embodiments of the presentinvention, respectively. The artificial mark shown in FIG. 2A is formedwith first through fourth polygons 221 through 227 that are arrangedone-dimensionally on a plane board 210 with a predetermined height (H)and width (W), and have colors different to each other. As the first andsecond embodiments shown in FIGS. 1A and 1B, in FIG. 2A, the shapes ofthe first through fourth polygons 221 through 227 are round and thenumber of polygons is 4, but the mark is not limited to this and thenumber of arbitrary polygons may be properly determined. Here, bychanging the arrangement order of the first through fourth polygons 221through 227, 24 (=4!) shapes can be expressed.

The artificial mark shown in FIG. 2B is formed with first through fourthpolygons 241 through 247 that are arranged one-dimensionally on a planeboard 230 with a predetermined height (H) and width (W), and have colorsdifferent to each other, and side quadrangles 251 and 253 disposed onboth sides of the plane board 230. As the third embodiment shown in FIG.2A, in FIG. 2B, the shapes of the first through fourth polygons 241through 247 are round and the number of polygons is 4, but the mark isnot limited to this and the number of arbitrary polygons may be properlydetermined.

The artificial mark shown in FIGS. 1A, 1B, 2A and 2B may have differentcombinations of polygons' colors, depending on locations within thedriving place. In the self-localizing apparatus and the self-localizingmethod according to the present invention, projection invariantinformation which is not affected by peripheral environments or noiseupon extraction of location information is used, and accordingly, anaccurate location can be determined even if the artificial mark imageacquired by an autonomous vehicle is distorted.

FIG. 3 is a block diagram showing the structure of an example of anintelligent system to which the present invention is applied. Theintelligent system is broadly formed with an image pickup unit 310, animage processing unit 330, a main control unit 350, and a drivingcontrol unit 370. Here, an example having a driving means such asdriving wheels will be explained as an autonomous vehicle 390. The maincontrol unit 350 includes a projective invariant calculator 361, asearch unit 363, and a position information analyzer 365.

Referring to FIG. 3, the image pickup unit 310 may be a standardcharge-coupled device (CCD) camera or a web camera in which an Internetserver and a camera are combined. Web cameras generate distorted imagesquite frequently as compared with general CCD cameras, but can be easilypopularized by virtue of their low prices. Because the autonomousvehicle 390 according to the present invention uses projection invariantinformation which is not affected by peripheral environments or noiseupon recognition of a landmark, excellent recognition results can beobtained even if a low-priced web camera is used instead of an expensiveCCD camera as the image pickup unit 310. Thus, self-localization of anautonomous vehicle can be economically accomplished. The image pickupunit 310 is installed on, for example, the top of the autonomous vehicle390. The image pickup unit 310 takes images of a driving place, andprovides the images taken to the image processing unit 330.

The image processing unit 330 detects an artificial mark used in thepresent invention, from the image provided by the image pickup unit 310.In the example of the artificial mark shown in FIG. 1A, the imageprocessing unit 330 detects the plane board 110 containing the firstthrough fifth polygons 121 through 129, from the taken image, based onConditional Density Propagation (CONDENSATION) algorithm, and thendetects the first through fifth polygons 121 through 129. TheCONDENSATION algorithm is explained in detail by M. Isard and A. Blakein “Contour tracking by stochastic propagation of conditional density”in Eur. Conf. on Computer Vision (ECCV), pp. 343-356,1996, and“Condensation-conditional density propagation for visual tracking” inInternational Journal of Computer Vision, 29 (1): 5-28, 1998. As such,the image processing unit 330 detects an artificial mark from the takenimage and provides the coordinate values of the first through fifthpolygons according to the detection result, to the main control unit350. The detection algorithm of the artificial mark is not limited tothe CONDENSATION algorithm and algorithms such as a line fitting or acircle fitting may be used.

The main control unit 350 performs an overall control action to operatethe autonomous vehicle 390 and performs an algorithm based on a methodof determining the position of the autonomous vehicle 390 according tothe present invention. By using the coordinate values of the firstthrough fifth polygons of the artificial mark provided by the imageprocessing unit 330, the projective invariant calculator 361 calculatesthe projective invariant of the detected artificial mark. Thus, when theprojective invariant is used, the artificial mark can be robustlyrecognized irrespective of a linear characteristic that can appear in ataken image, or the slope angle of the artificial mark.

The search unit 363 obtains a projective invariant of each individualartificial mark installed in a driving place of the autonomous vehicle390, indexes a global location of the artificial mark in the drivingplace corresponding to the obtained projective invariant, and thenstores a global location of the artificial mark and its projectiveinvariant with the index, in a database in advance. By using thecalculated projective invariant provided from the projective invariantcalculator 361, the database is searched and the global location of theartificial mark in the driving place corresponding to the calculatedprojective invariant is obtained.

The position information analyzer 365 analyses the global location inthe driving place and the direction angle of the autonomous vehicle 390by using the global location in the driving place in which the detectedartificial mark is disposed, and the distance information between theimage pickup unit 310 and the detected artificial mark. By using theanalyzed global location in the driving place and the direction angle ofthe autonomous vehicle 390, and based on a moving path program which isinput in advance, the position information analyzer 365 determines themoving direction and speed of the autonomous vehicle 390, and providesthe determined moving direction and speed as a moving command, to thedriving control unit 370.

The driving control unit 370 controls the moving direction and speed ofthe autonomous vehicle 390 based on the moving command provided by theposition information analyzer 365. That is, the control unit 370provides a command to a driving apparatus, such as a driving motor, suchthat based on the moving path program input in advance, the autonomousvehicle 390 moves in the next position. By doing so, moving of theautonomous vehicle 390, such as going forward, backward, or changingdirection, is controlled.

FIG. 4 is a view of a pin-hall camera model of the image pickup unit 310of FIG. 3. Referring to FIG. 4, a projective transformation for an imagein the pin-hall camera model can be expressed as follows.

$\begin{matrix}{\begin{bmatrix}u \\v \\1\end{bmatrix} = {{\frac{1}{{t_{31}X} + {t_{32}Y} + {t_{33}Z} + t_{34}}\begin{bmatrix}t_{11} & t_{12} & t_{13} & t_{14} \\t_{21} & t_{22} & t_{23} & t_{24} \\t_{31} & t_{32} & t_{33} & t_{34}\end{bmatrix}}\begin{bmatrix}X \\Y \\Z \\1\end{bmatrix}}} & (1)\end{matrix}$

where (u, v, 1) denotes a coordinates of a point q defined on the imageplane, (X, Y, Z, 1) denotes a coordinates of a point P in an objectcoordinate system, and t_(ij) denotes an ij factor of a transformationmatrix between an object plane and the image plane.

Here, if an object is projected to on a two-dimensional plane, i.e.,Z=0, Equitation 1 is transformed as follows.

$\begin{matrix}{\begin{bmatrix}u \\v \\1\end{bmatrix} = {{\frac{1}{{t_{31}X} + {t_{32}Y} + t_{34}}\begin{bmatrix}t_{11} & t_{12} & t_{14} \\t_{21} & t_{22} & t_{24} \\t_{31} & t_{32} & t_{34}\end{bmatrix}}\begin{bmatrix}X \\Y \\1\end{bmatrix}}} & (2)\end{matrix}$

As shown in Equations 1 and 2, the process for obtaining an image isperformed in non-linear environment. However, a linear projectivetransformation can be applied to a two-dimensional image obtainedthrough the image pickup unit 310 rather than a non-linear projectivetransformation like in Equation 2, as shown in FIG. 5.

FIG. 5 is a view showing conditions to obtain a linear model of theimage pickup unit 310 of FIG. 3. As shown in FIG. 5, if a length S fromthe image pickup unit 310 to the object is sufficiently longer than asize S₀ of the object, the non-linear projective transformation fromlike Equation 2 is transformed as follows.

$\begin{matrix}{\begin{bmatrix}u \\v \\1\end{bmatrix} \approx {{S\begin{bmatrix}t_{11} & t_{12} & t_{14} \\t_{21} & t_{22} & t_{24} \\t_{31} & t_{32} & t_{34}\end{bmatrix}}\begin{bmatrix}X \\Y \\1\end{bmatrix}}} & (3)\end{matrix}$

A Fourier descriptor is a linearized shape descriptor which satisfiesEquations 1, 2, and 3. The Fourier descriptor represents an image of theobject with Fourier coefficients which are obtained by a two-dimensionalFourier transformation for the image contour of a two-dimensionalobject. However, this method can be applied only to a case where thelinearity of the image pickup unit 310 is guaranteed, that is, where adistance between the image pickup unit 310 and the object is too long.Therefore, to overcome the restriction, the image obtained from theimage pickup unit 310 is analyzed by using a projective invariant in thepresent invention. As a result, even in a case where the linearity ofthe image pickup unit 310 is not guaranteed, that is, the distancebetween the image pickup unit 310 and the object is not long, the imagecan be analyzed correctly without being affected by noise, slant angles,or the non-linearity of the image pickup unit 310 occurring when imagesare obtained.

FIG. 6 is a flowchart of the steps performed by a position recognitionmethod of an intelligent system according to the present invention.

Referring to FIG. 6, a quadrangle plane board indicating an artificialmark is detected in operation 610, and first through fifth polygonsdisposed in the quadrangle plane board are detected in operation 620. Atthis time, preferably, the CONDENSATION algorithm is used. Thus, whenthe CONDENSATION algorithm is used, the results of detection when anartificial mark is rotated, slanted, translated, and scaled, andincluded in a plurality of objects is shown in FIG. 13.

In operation 630, the projective invariant of the first through fifthpolygon detected in the operation 620 is calculated. The projectiveinvariant of X-axis can be calculated by det(•) value using X-axiscoordinate values of the first through fifth polygons arranged2-dimensionally, on an image plane and an object plane or by thecrossing ratio of X-axis coordinate values of the first through fourthpolygons arranged one-dimensionally, on an image plane and an objectplane. Similarly, the projective invariant of Y-axis can be calculatedby det(•) value using Y-axis coordinate values of the first throughfifth polygons arranged 2-dimensionally, on an image plane and an objectplane or by the crossing ratio of Y-axis coordinate values of the firstthrough fourth polygons arranged one-dimensionally, on an image planeand an object plane.

In operation 640, the global location of the detected artificial mark isrecognized by using the projective invariant calculated in the operation630. For this, the index, projective invariant, and global locationinformation in the driving place of each of shapes of artificial markswhich are disposed at respective locations of the driving place inadvance, are prepared in advance in a database.

In operation 650, by using the global location information in thedriving place of the artificial mark recognized in the operation 640,and distance information between the image pickup unit 310 of theautonomous vehicle 390 and the artificial mark, the global location ofthe autonomous vehicle 390 is analyzed.

In operation 660, by using the location information of the autonomousvehicle 390 obtained in the operation 650, moving of the autonomousvehicle 390 is controlled along the preprogrammed moving path in adesired direction.

FIG. 7 shows a 2-dimensional artificial mark installed in a drivingplace, the coordinate system on a taken image, and the projectiverelation. A projective invariant I is calculated by using Equation 4:

$\begin{matrix}{I = {\frac{{\det \left( {q_{5}q_{1}q_{4}} \right)}{\det \left( {q_{5}q_{2}q_{3}} \right)}}{{\det \left( {q_{5}q_{1}q_{3}} \right)}{\det \left( {q_{5}q_{2}q_{4}} \right)}} = \frac{{\det \left( {P_{5}P_{1}P_{4}} \right)}{\det \left( {P_{5}P_{2}P_{3}} \right)}}{{\det \left( {P_{5}P_{1}P_{3}} \right)}{\det \left( {P_{5}P_{2}P_{4}} \right)}}}} & (4)\end{matrix}$

wherein P denotes coordinates of a point indicating one of the firstthrough fifth polygons, and q is coordinates of a point on a taken imagecorresponding to P (see FIG. 7). Det(•) in Equation 4 is defined as inEquation 5:

$\begin{matrix}{{{\det \left( {q_{1}q_{2}q_{3}} \right)} = {f\begin{bmatrix}x_{1} & x_{2} & x_{3} \\y_{1} & y_{2} & y_{3} \\1 & 1 & 1\end{bmatrix}}}{{\det \left( {P_{1}P_{2}P_{3}} \right)} = {{f\begin{bmatrix}X_{1} & X_{2} & X_{3} \\Y_{1} & Y_{2} & Y_{3} \\1 & 1 & 1\end{bmatrix}} = {2^{k}\left( {{Area}\mspace{14mu} {of}\mspace{14mu} \Delta \; P_{1}P_{2}P_{3}} \right)}}}} & (5)\end{matrix}$

Here, f denotes a focal distance. A projective invariant calculated bythe equation 4 is a value that is constant even under a nonlinearchange, and can be effectively used for detection of an artificial mark.

The projective invariants calculated by the equation 4 are shown inrelation to the shapes of artificial marks in FIG. 9. FIG. 9 showsexamples of 8 shapes.

FIG. 8 shows a 1-dimensional artificial mark installed in a drivingplace, the coordinate system on a taken image, and the projectiverelation. At this time, the projective invariant (I(x₁, x₂, x₃, x₄)) isexpressed as Equation 6:

$\begin{matrix}\begin{matrix}{I = \frac{\left( {x_{1} - x_{2}} \right)\left( {x_{3} - x_{4}} \right)}{\left( {x_{1} - x_{3}} \right)\left( {x_{2} - x_{4}} \right)}} \\{= \frac{\left( {X_{1} - X_{2}} \right)\left( {X_{3} - X_{4}} \right)}{\left( {X_{1} - X_{3}} \right)\left( {X_{2} - X_{4}} \right)}}\end{matrix} & (6)\end{matrix}$

In Equation 6, (X₁, X₂, X₃, X₄) and (x₁, x₂, x₃, x₄) denote X-axiscoordinate values of 4 points on straight lines defined on an objectplane and on an image plane, respectively. Likewise, by applying theequation 6, projective invariant (I(y₁, y₂, y₃, y₄)) can also becalculated.

That is, though the distance between two points changes under projectivechange, the projective invariant obtained by Equation 6 is a constantvalue and is used for recognition of an artificial mark.

FIGS. 10A through 10D are diagrams showing the results of experiments ofartificial mark recognition according to the present invention. Theartificial mark shown in FIGS. 10A, 10B, and 10D is the seventh shape(#7) of artificial mark examples shown in FIG. 9, and the artificialmark shown in FIG. 10C is the eighth shape (#8) of artificial markexamples shown in FIG. 9. Thus, in an example of the artificial markshown in FIG. 1B, with the plane board 130 formed as a large quadrangle,the depth, deviation, and angle between the image pickup unit 310installed on the autonomous vehicle 390 and the artificial mark can beidentified. With the side quadrangles 151 and 153, the direction inwhich the image pickup unit 310 installed on the autonomous vehicle 390looks at the artificial mark can be identified.

FIG. 11 is a diagram explaining an embodiment of a method for analyzingthe location information of the autonomous vehicle in FIG. 6. Thegeometrical relation of the artificial mark and its taken image can beexpressed by Equations 7 and 8:

$\begin{matrix}{{{\Delta \; \upsilon} = {f_{y}\frac{{Mark}\mspace{14mu} {Height}}{Depth}}}{{Depth} = {f_{y}\frac{{Mark}\mspace{14mu} {Height}}{\Delta \; \upsilon}}}} & (7) \\{{{\Delta \; u} = {f_{x}\frac{{Mark}\mspace{14mu} {Width} \times \cos \; \theta}{Depth}}}{{\cos \; \theta} = \frac{{Depth} \times \Delta \; u}{f_{x}\mspace{14mu} {Mark}\mspace{14mu} {Width}}}} & (8)\end{matrix}$

wherein Δu and Δv denote the width and height of the artificial markdetected in an image obtained by the image pickup unit 310, that is, acamera, respectively. f_(x) and f_(y) denote a scale factor which isdefined by a specification of the camera or through a calibrationprocess and by which a pixel unit in the image obtained by the imagepickup unit 310 converts into a unit such as centimeter (cm). Also, markheight and mark width denote the width and height of the artificial markdisposed in a driving place, respectively. That is, the distance (depth)between the mark and the autonomous vehicle can be obtained fromEquation 7, and the angle (cos θ) in which the autonomous vehicle looksat the mark can be obtained from Equation 8.

FIG. 12 is a diagram explaining another embodiment of a method foranalyzing the location information of the autonomous vehicle in FIG. 6.That is, FIG. 12 shows a process of extracting information on thedistance and orientation of an artificial mark with respect to theautonomous vehicle 390 from data on the first through fifth polygons 121through 129 which are extracted from the artificial mark image projectedby a camera.

Examples 1203 a through 1203 c of the first through fifth polygons 121through 129 obtained from the artificial mark image acquired by a cameraare shown in FIG. 12. Referring to FIG. 12, the shape of the firstthrough fifth polygons 121 through 129 of the artificial mark can be aperfect circle 1203 c, an oval 1203 b, or an oval 1203 a inclined by apredetermined angle because of the non-linearity of the camera.

Such a circular or oval figure can be expressed as in an equation of aquadratic section having two parameters x and y and is referred to as aconic section. A conic section including a perfect circle and an ovalcan be expressed in an implicit equation such as Equation 9:

S(x,y)=Ax ²+2Bxy+Cy ²+2(Dx+Ey)+F=0  (9)

A conic section projected by a camera, that is, a second oval outerline, can be expressed in a matrix format such as Equation 10:

$\begin{matrix}{Q = {k\begin{pmatrix}A & B & {D/f} \\B & C & {E/f} \\{D/f} & {E/f} & {F/f^{2}}\end{pmatrix}}} & (10)\end{matrix}$

wherein f denotes the focal length of the camera, and k denotes anarbitrary non-zero constant.

If a conic section expressed as in Equations 9 and 10 rotates around anarbitrary axis, the relationship between an arbitrary cubic equation (Q)and a cubic equation (Q′) for the rotated conic section is expressed asin Equation 11:

Q′=k′R^(T)QR  (11)

wherein R denotes a rotation matrix.

According to the relationship expressed in Equation 11,three-dimensional information (e.g., distance and orientationinformation) between an artificial mark and the autonomous vehicle 390can be extracted when an artificial mark image is acquired by theautonomous vehicle 390.

If the first through fifth polygons 121 through 129 of an artificialmark has the oval shape 1203 a inclined by a predetermined angle, thecubic equation for the oval shape 1203 a is the same as Equation 10. Ifthe oval shape 1203 a is transformed into an oval located at a standardposition as the oval 1203 b of FIG. 12, the cubic equation (Q) for theoval 1203 a expressed in Equation 10 is transformed into Equation 12:

$\begin{matrix}{Q^{\prime} = {k^{\prime}\begin{pmatrix}1 & O & O \\O & \alpha & O \\O & O & {{- \gamma}/f^{2}}\end{pmatrix}}} & (12)\end{matrix}$

The relationship equation of two cubic equations Q and Q′ is given byEquation 13:

Q′=k′U^(T)QU  (13)

wherein U is equal to [U₁, U₂, U₃] and denotes a matrix comprised ofeigen vectors for an eigen value of a conic equation Q, λ₁, λ₂, λ₃.

A cubic equation used to transform an oval as the oval 1203 b of FIG. 12into a perfect circle as the perfect circle 1203 c of FIG. 12 is givenby Equation 14:

$\begin{matrix}{Q^{\prime\prime} = {k^{\prime\prime}\begin{pmatrix}1 & O & O \\O & 1 & {c/f} \\O & {c/f} & {\left( {c^{2} - \rho^{2}} \right)/f^{2}}\end{pmatrix}}} & (14)\end{matrix}$

The relationship equation between the cubic equation (Q) of theoriginally acquired landmark image and the finally-transformed cubicequation (Q″) is given by Equation 15:

Q″=k″R^(T)QR  (15)

wherein R denotes a rotation transformation matrix for axis x′.

As described above, by transforming the cubic equation for a conicsection (that is, the first through fifth polygons 121 through 129)extracted from an artificial mark image acquired by a camera,information on the orientation between an artificial mark and theautonomous vehicle 390 and information on the distance therebetween areobtained using the relationship equation between the cubic equationextracted from the original acquired artificial mark image and each ofthe transformed cubic equations Q′ and Q″. The distance informationbetween an artificial mark and the autonomous vehicle 390 is obtainedusing a normal vector (n′) on the image plane where the perfect circle1203 c of FIG. 12 into which the oval 1203 b is transformed, a vector(ct′) to the center of the perfect circle 1203 c, and a normal distance(d′) to the center of the perfect circle 1203 c. A normal vector (n) forthe oval 1203 a and a vector (ct) and a normal distance (d) to thecenter of the oval 1203 a are obtained from the normal vector (n′),vector (ct′), and the normal distance (d′) and expressed as in Equations16, 17, and 18, respectively:

n=URn′  (16)

ct=URct′  (17)

d=λ ₁ ^(3/2)γ  (18)

wherein n′ is (0 0 1)^(T), ct′ is (0−dc/fd)^(T), c is √{square root over((α−1)(γ+f²))}{square root over ((α−1)(γ+f²))}, α is

$\frac{\lambda_{2}}{\lambda_{1}},$

and γ is

${- f^{2}}{\frac{\lambda_{2}}{\lambda_{1}} \cdot {c\left( {= \sqrt{\left( {\alpha - 1} \right)\left( {\gamma + f^{2}} \right)}} \right)}}$

denotes a value used to compensate for the difference between the centercoordinate values of the perfect circle 1203 c and the oval 1203 b.Information on the distance between the artificial mark image acquiredby a camera and the autonomous vehicle 390 can be obtained by tracingequations backward from the equation for the final perfect circle 1203 cto the equation for the original oval 1203 a.

While the present invention has been particularly shown and describedwith reference to exemplary embodiments concerning self-localization ofautonomous vehicles such as mobile robots, the present invention is alsoapplicable to image recognition and tracking performed in imageapplication fields, such as automation systems, intelligent systems, andthe like.

The invention can also be embodied as computer readable codes on acomputer readable recording medium. The computer readable recordingmedium is any data storage device that can store data which can bethereafter read by a computer system. Examples of the computer readablerecording medium include read-only memory (ROM), random-access memory(RAM), CD-ROMs, magnetic tapes, floppy disks, optical data storagedevices, and carrier waves (such as data transmission through theInternet). The computer readable recording medium can also bedistributed over network coupled computer systems so that the computerreadable code is stored and executed in a distributed fashion. Also,functional programs, codes, and code segments for accomplishing thepresent invention can be easily construed by programmers skilled in theart to which the present invention pertains.

According to the present invention as described above, artificial marksare formed with a plurality of polygons each having a different colorand arranged one-dimensionally or 2-dimensionally and installed atarbitrary locations on a driving place. Then, by using the camera of anautonomous vehicle, the artificial mark is detected and recognized, andby using the recognized global location information of the artificialmark and the distance information between the camera and the artificialmark, the location of the autonomous vehicle is obtained. Accordingly,the time taken for detecting the artificial mark is very short. Also,since the artificial mark and the shape of the mark can be accuratelydetected even when there is a nonlinear characteristic in a taken image,separate manipulation of the camera of the autonomous vehicle is notneeded in order to take the image of the artificial mark.

While the present invention has been particularly shown and describedwith reference to exemplary embodiments thereof, it will be understoodby those of ordinary skill in the art that various changes in form anddetails may be made therein without departing from the spirit and scopeof the present invention as defined by the following claims.

1. An intelligent system comprising: an image pickup unit which obtainsan image taken for a driving place; a main control unit which calculatesa projective invariant of an artificial mark detected from an imagetaken for a driving place and analyzes the position of the intelligentsystem using global location information of the detected artificial markin the driving place obtained by the calculated projective invariant andlocation information between the intelligent system and the detectedartificial mark; and a driving control unit which controls driving ofthe intelligent system according to the position information of theintelligent system analyzed in the main control unit.
 2. The intelligentsystem of claim 1, wherein the main control unit comprises: a projectiveinvariant calculator which calculates a projective invariant of thedetected artificial mark; a search unit which stores a database ofindices according to a combination of colors of polygons included in theartificial mark, projective invariants of the artificial marks, andglobal location information of the artificial marks in the drivingplace, and searches the database by the calculated projective invariantfor obtaining the global location information of the detected artificialmark; and a position information analyzer which analyzes the position ofthe intelligent system by using the global location information of thedetected artificial mark and location information between theintelligent system and the detected artificial mark.
 3. The intelligentsystem of claim 1, wherein the artificial mark is formed with a planewith a first color, and a plurality of polygons arranged in the planewith a form of one-dimension or two-dimension, having colors differentfrom the first color and different to each other.
 4. The intelligentsystem of claim 3, wherein the artificial mark further comprises areason both sides of the plane, the areas having a color different from thecolors of the plane and polygons.
 5. The intelligent system of claim 1,wherein the main control unit obtains information on the orientation anddistance of the intelligent system with respect to the detectedartificial mark by using information on the height and width of thedetected artificial mark, a scaling factor and the height and width ofthe artificial mark installed on the driving place.
 6. The intelligentsystem of claim 1, wherein if the real shape of polygons included in anartificial mark is perfect circle and the shape of polygons included inthe detected artificial mark is an oval, the main control unittransforms an equation for a cubic section of the oval into a cubicsection equation for the perfect circle and obtains the locationinformation on the orientation and distance of the intelligent systemwith respect to the detected artificial mark from the relationshipequation between the two conic section equations.
 7. A method ofcontrolling an intelligent system using an artificial mark comprising:obtaining an image taken for a driving place; calculating a projectiveinvariant of an artificial mark detected from the image taken for thedriving place and analyzing the position of the intelligent system usingglobal location information of the detected artificial mark in thedriving place obtained by the calculated projective invariant andlocation information between the intelligent system and the detectedartificial mark; and controlling driving of the intelligent systemaccording to the position information of the intelligent system.
 8. Themethod of claim 7, further comprising: providing a database of indicesaccording to a combination of colors of polygons included in theartificial mark, projective invariants of the artificial marks; andsearching the database by the calculated projective invariant forobtaining the global location information of the detected artificialmark.
 9. The method of claim 7, wherein the artificial mark is formedwith a plane with a first color, and a plurality of polygons arranged inthe plane with a form of one-dimension or two-dimension, having colorsdifferent from the first color and different to each other.
 10. Themethod of claim 9, wherein the artificial mark further comprises areason both sides of the plane, the areas having a color different from thecolors of the plane and the plurality of polygons.
 11. The method ofclaim 7, wherein the analyzing of the position of the intelligent systemobtains information on orientation and distance of the intelligentsystem with respect to the detected artificial mark by using informationon height and width of the detected artificial mark, a scaling factorand the height and width of the artificial mark installed on the drivingplace.
 12. The method of claim 7, wherein if a real shape of polygonsincluded in an artificial mark is a circle and a shape of polygonsincluded in the detected artificial mark is an oval, the analyzing ofthe position of the intelligent system transforms an equation for acubic section of the oval into a cubic section equation for the circleand obtains information on the orientation and distance of theintelligent system with respect to the detected artificial mark from therelationship equation between the two conic section equations.
 13. Acomputer readable recording medium having embodied thereon a computerprogram for a method of controlling an intelligent system using anartificial mark comprising: obtaining an image taken for a drivingplace; calculating a projective invariant of an artificial mark detectedfrom the image taken for the driving place and analyzing the position ofthe intelligent system using global location information of the detectedartificial mark in the driving place obtained by the calculatedprojective invariant and location information between the intelligentsystem and the detected artificial mark; and controlling driving of theintelligent system according to the position information of theintelligent system.